Power supply processing method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a power supply processing method and device, electronic equipment and storage medium. The method comprises: obtaining historical power consumption data information and power consumption demand information, inputting the historical power consumption data information and the power consumption demand information into a prediction model for processing to obtain a power consumption prediction curve; wherein the power consumption prediction curve represents predicted power consumption data in different time periods; determining a target power supply strategy for a power consumption device according to the power consumption prediction curve, cost information and a preset constraint condition; wherein the target power supply strategy represents supplying power to the power consumption device through different power supply modes in different time periods, and the different power supply modes include: mains power supply, energy storage device power supply and generator power supply; and controlling at least one of the mains, the energy storage device and the generator to supply power to the power consumption device according to the target power supply strategy. The method is used to improve the rationality of the power supply scheme.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a power supply processing method, apparatus, electronic device and storage medium. Background Technology

[0002] With the rapid development of society and the economy, many electricity consumption scenarios require short-term high power supply to electrical equipment. Specifically, the power supply requirements for electrical equipment vary in different electricity consumption scenarios, necessitating power supply solutions tailored to the specific needs of each scenario.

[0003] Some technologies employ a static capacity allocation model, configuring the power supply scheme based on the maximum power load in a power consumption scenario, which can ensure reliable power supply to electrical equipment in that scenario. However, the aforementioned technologies have the problem of being unreasonable in supplying power to electrical equipment through a power supply scheme configured according to the maximum power load.

[0004] Therefore, there is an urgent need for a solution that can provide reasonable power supply based on the power supply needs of different power consumption scenarios. Summary of the Invention

[0005] The power supply processing method, apparatus, electronic equipment, and storage medium provided in the embodiments of this application are used to improve the rationality of the power supply scheme.

[0006] In a first aspect, embodiments of this application provide a power supply processing method, including:

[0007] Historical electricity consumption data and electricity demand information are obtained, and then input into the prediction model for processing to obtain the electricity consumption prediction curve; the electricity consumption prediction curve represents the predicted electricity consumption data in different time periods.

[0008] Based on electricity consumption forecast curves, cost information, and preset constraints, the target power supply strategy for electrical equipment is determined. The target power supply strategy represents the supply of power to electrical equipment through different power supply methods at different times. The different power supply methods include: mains power supply, energy storage device power supply, and generator power supply.

[0009] According to the target power supply strategy, control at least one of the mains power, energy storage equipment and generator to supply power to the electrical equipment.

[0010] In one possible implementation, a target power supply strategy for electrical equipment is determined based on electricity consumption forecast curves, cost information, and preset constraints, including:

[0011] Based on a preset optimization function, and according to the electricity consumption forecast curve, cost information and preset constraints, iterative optimization is performed to obtain the target power supply strategy for the electrical equipment.

[0012] The optimization function represents the total cost of supplying power to electrical equipment; the target power supply strategy is the power supply strategy with the minimum total cost represented by the optimization function among multiple power supply strategies; the constraints include at least one of the following: the power limit of the power supply equipment, the allowable range of remaining energy storage of the energy storage equipment, and the minimum operating time of the generator; the cost information includes at least one of the following: the time-of-use electricity price of the grid, the fuel cost of the generator, and the depreciation cost of the power supply equipment, which includes energy storage equipment and generator.

[0013] In one possible implementation, the method further includes:

[0014] Acquire temperature and humidity data of the environment in which the energy storage device is located;

[0015] The allowable range of remaining energy storage capacity of the energy storage device is adjusted based on temperature and humidity data.

[0016] In one possible implementation, according to a target power supply strategy, controlling at least one of mains power, energy storage devices, and generators to supply power to electrical devices includes:

[0017] According to the target power supply strategy, the connection switch for connecting the mains power to the electrical equipment is opened to enable the mains power to supply power to the electrical equipment; and / or,

[0018] According to the target power supply strategy, the energy storage device is controlled to operate in discharge mode to enable the energy storage device to supply power to the electrical equipment; and / or,

[0019] According to the target power supply strategy, the generator is controlled to start so that it can supply power to the electrical equipment.

[0020] In one possible implementation, the method further includes:

[0021] Based on the usage records of the power supply equipment, an electronic ledger for the power supply equipment is generated; the power supply equipment includes energy storage equipment and generators.

[0022] The electronic ledger is stored in the blockchain, and at least one preset action is performed on the power supply equipment based on the electronic ledger in the blockchain; wherein the blockchain includes multiple nodes, including: equipment user node, equipment provider node, and equipment testing node.

[0023] In one possible implementation, at least one preset action includes at least one of the following actions:

[0024] When the service life of the power supply equipment is reached, a recycling command is sent to the power supply equipment to lock it.

[0025] Send the electronic ledger of the power supply equipment to the equipment testing node so that the equipment testing node can perform a health assessment of the power supply equipment and obtain the health assessment score of the power supply equipment.

[0026] When the health assessment score is less than a preset threshold, the status of the power supply equipment will be changed to unavailable.

[0027] In one possible implementation, the method further includes:

[0028] Based on the electronic ledger of the power supply equipment, the behavior evaluation processes of the equipment user nodes and equipment provider nodes in the blockchain are carried out to obtain the behavior scores of the equipment user nodes and the behavior scores of the equipment provider nodes.

[0029] Secondly, embodiments of this application provide a power supply processing device, comprising:

[0030] The acquisition module is used to acquire historical electricity consumption data and electricity demand information, input the historical electricity consumption data and electricity demand information into the prediction model for processing, and obtain the electricity consumption prediction curve; wherein, the electricity consumption prediction curve represents the predicted electricity consumption data in different time periods;

[0031] The processing module is used to determine the target power supply strategy for electrical equipment based on the electricity consumption forecast curve, cost information, and preset constraints. The target power supply strategy represents the power supply to the electrical equipment through different power supply methods at different times. The different power supply methods include: mains power supply, energy storage device power supply, and generator power supply.

[0032] The control module is used to control at least one of the following—mains power, energy storage devices, and generators—to supply power to electrical devices according to a target power supply strategy.

[0033] In one possible implementation, based on electricity consumption forecast curves, cost information, and preset constraints, a target power supply strategy for the electrical equipment is determined. The processing module is used to:

[0034] Based on a preset optimization function, and according to the electricity consumption forecast curve, cost information and preset constraints, iterative optimization is performed to obtain the target power supply strategy for the electrical equipment.

[0035] The optimization function represents the total cost of supplying power to electrical equipment; the target power supply strategy is the power supply strategy with the minimum total cost represented by the optimization function among multiple power supply strategies; the constraints include at least one of the following: the power limit of the power supply equipment, the allowable range of remaining energy storage of the energy storage equipment, and the minimum operating time of the generator; the cost information includes at least one of the following: the time-of-use electricity price of the grid, the fuel cost of the generator, and the depreciation cost of the power supply equipment, which includes energy storage equipment and generator.

[0036] In one possible implementation, the acquisition module is also used to acquire temperature and humidity data of the environment in which the energy storage device is located;

[0037] The processing module is also used to adjust the allowable range of remaining energy storage of the energy storage device based on temperature and humidity data.

[0038] In one possible implementation, according to a target power supply strategy, at least one of mains power, energy storage devices, and generators is controlled to supply power to the electrical equipment. The control module is used to:

[0039] According to the target power supply strategy, the connection switch for connecting the mains power to the electrical equipment is opened to enable the mains power to supply power to the electrical equipment; and / or,

[0040] According to the target power supply strategy, the energy storage device is controlled to operate in discharge mode to enable the energy storage device to supply power to the electrical equipment; and / or,

[0041] According to the target power supply strategy, the generator is controlled to start so that it can supply power to the electrical equipment.

[0042] In one possible implementation, the processing module is further configured to:

[0043] Based on the usage records of the power supply equipment, an electronic ledger for the power supply equipment is generated; the power supply equipment includes energy storage equipment and generators.

[0044] The electronic ledger is stored in the blockchain, and at least one preset action is performed on the power supply equipment based on the electronic ledger in the blockchain; wherein the blockchain includes multiple nodes, including: equipment user node, equipment provider node, and equipment testing node.

[0045] In one possible implementation, at least one preset action includes at least one of the following actions:

[0046] When the service life of the power supply equipment is reached, a recycling command is sent to the power supply equipment to lock it.

[0047] Send the electronic ledger of the power supply equipment to the equipment testing node so that the equipment testing node can perform a health assessment of the power supply equipment and obtain the health assessment score of the power supply equipment.

[0048] When the health assessment score is less than a preset threshold, the status of the power supply equipment will be changed to unavailable.

[0049] In one possible implementation, the processing module is further configured to:

[0050] Based on the electronic ledger of the power supply equipment, the behavior evaluation processes of the equipment user nodes and equipment provider nodes in the blockchain are carried out to obtain the behavior scores of the equipment user nodes and the behavior scores of the equipment provider nodes.

[0051] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0052] The memory stores the instructions that the computer executes;

[0053] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0054] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0055] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0056] The power supply processing method, apparatus, electronic device, and storage medium provided in this application predict the power consumption data of electrical equipment at different times in a given power consumption scenario based on historical power consumption data and power demand information. Based on the prediction results, a target power supply strategy for the electrical equipment is generated with preset optimization objectives and constraints. Finally, power is supplied to the electrical equipment using different power supply methods based on this target power supply strategy. The solution provided in this application improves the rationality of the power supply scheme for electrical equipment by combining dynamic prediction with optimization algorithms. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0058] Figure 1 Flowchart of the power supply processing method provided in this application Figure 1 ;

[0059] Figure 2 Flowchart of the power supply processing method provided in this application Figure 2 ;

[0060] Figure 3 Flowchart of the power supply processing method provided in this application Figure 3 ;

[0061] Figure 4 A schematic diagram of the power supply processing device provided in this application;

[0062] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0063] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0064] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0065] First, let me explain the terms used in this application:

[0066] Mains electricity: refers to the electricity used by urban residents, that is, the alternating current supplied by the public power grid to electrical equipment in different power consumption scenarios.

[0067] Energy storage devices are devices used to store electrical energy and release it on demand. They are used to store energy when there is a surplus of electricity and to release energy during peak electricity demand or power outages. Common types of energy storage devices include electrochemical energy storage, mechanical energy storage, and electromagnetic energy storage.

[0068] Taking electrochemical energy storage as an example, energy storage devices can include lithium batteries or lead-acid batteries; taking mechanical energy storage as an example, energy storage devices can include flywheel energy storage or pumped hydro storage, etc.; taking electromagnetic energy storage as an example, energy storage devices can be supercapacitors.

[0069] Electrical equipment refers to devices that require power from electrical supply equipment to operate and perform relevant functions in temporary power supply scenarios. The power supply equipment can be the aforementioned energy storage devices or generators. It can be understood that power can be supplied to electrical equipment through either electrical supply equipment or mains power.

[0070] With the rapid development of the social economy, various potential temporary power supply scenarios exist. Specifically, these scenarios can include: construction sites, exhibition venues, and ensuring power supply for sporting events. These temporary power supply scenarios generally face challenges such as high investment in power equipment, long deployment cycles, and resource waste.

[0071] Therefore, for such temporary power supply scenarios, it is common practice to lease power supply equipment to supply power to the equipment in the actual power supply scenario.

[0072] This explanation addresses different temporary power usage scenarios, using building construction as an example. The power demand at construction sites exhibits significant phased characteristics. Specifically, during the foundation construction phase, the power load of equipment such as concrete mixing and rebar processing is high. During the main construction phase, the power load may be concentrated in vertical transportation equipment such as tower cranes and elevators. Once the finishing phase begins, the power load may be further distributed among multiple small power tools.

[0073] Taking an exhibition as an example, during the setup of an exhibition, a large number of lighting, air conditioning and display equipment may need to be turned on at the same time, and the electricity demand is concentrated.

[0074] Taking the scenario of ensuring power supply for sports events as an example, it is necessary to provide a stable power supply for electrical equipment such as stadium lighting, display screens and sound systems, and the power demand also has the characteristic of being concentrated.

[0075] In some embodiments, a static capacity allocation mode is adopted for power supply strategy formulation, that is, power supply equipment is configured all at once according to the maximum load of the power consumption scenario, and the power supply scheme is fixed. This can ensure the reliability of power supply from the power supply equipment to the power consumption equipment under extreme conditions, but it will lead to the idle resources of the power supply equipment during low load periods, and increase the leasing, procurement and operation and maintenance costs of the power supply equipment.

[0076] Based on the above scenarios, it can be seen that in the relevant embodiments, there is a technical problem of an unreasonable power supply strategy from the power supply equipment to the power consumption equipment.

[0077] The power supply processing method provided in this application predicts the power consumption data of electrical equipment at different times under a given power consumption scenario based on historical power consumption data and power demand information. Based on the prediction results, a target power supply strategy for the electrical equipment is generated with preset optimization objectives and constraints. Finally, power is supplied to the electrical equipment using different power supply methods based on this target power supply strategy. The solution provided in this application improves the rationality of the power supply scheme for electrical equipment by combining dynamic prediction with optimization algorithms.

[0078] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0079] Figure 1 Flowchart of the power supply processing method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0080] Step 101. Obtain historical electricity consumption data and electricity demand information, input the historical electricity consumption data and electricity demand information into the prediction model for processing, and obtain the electricity consumption prediction curve.

[0081] Among them, the electricity consumption forecast curve represents the predicted electricity consumption data for different time periods.

[0082] For example, historical electricity consumption data refers to the electricity load information recorded within a preset time period in the past for that specific electricity consumption scenario. Taking a building construction scenario as an example, it could be the power consumption curves at each construction stage.

[0083] Electricity demand information refers to an indication of electricity demand in the current or future time period. Taking an exhibition as an example, it could be the planned activation of lighting and air conditioning equipment during setup, indicating the electricity demand during the subsequent setup process.

[0084] Optionally, electricity demand information can be submitted by users through an online platform. Furthermore, this online platform can intelligently match idle equipment (transformers, energy storage devices, generators). Based on parameters such as user electricity capacity, geographical location, and leasing period, combined with equipment inventory status, a greedy algorithm is used to optimize equipment allocation and reduce logistics costs.

[0085] Greedy algorithms, in particular, select the optimal decision at each step without considering the overall impact. In the scenario described above, each step prioritizes the device with the highest cost-effectiveness (e.g., the device closest to the user and with the lowest rental cost), gradually combining these options to form feasible solutions. This approach can generate relatively optimal solutions in a short time, making it suitable for rapid decision-making in dynamic rental scenarios.

[0086] First, historical electricity consumption data and electricity demand information are acquired and input into a prediction model for processing, generating an electricity consumption forecast curve. This curve represents the predicted electricity consumption data for different time periods. This forecast curve can be used to generate an optimal power supply strategy. The generated power supply strategy is the power supply strategy for electrical equipment, enabling power supply equipment or mains power to supply power to the equipment.

[0087] For example, a prediction model is a pre-trained computational model that uses algorithms to extract features from input data and generate prediction results. For instance, a prediction model could be an LSTM (Long Short-Term Memory) model.

[0088] Optionally, the input parameters of the LSTM model may include historical electricity consumption data and electricity demand information. Taking a building construction scenario as an example, the electricity demand information can be the electricity consumption characteristics of different construction stages (such as foundation construction stage and main structure construction stage). Optionally, the input of the LSTM model may also include weather information (such as temperature and humidity data).

[0089] The LSTM model analyzes and processes the input parameters using a forget gate, input gate, and output gate in its gating mechanism to generate an electricity consumption forecast curve. This electricity consumption forecast curve can be used to guide the generation of time-of-use power supply plans.

[0090] The forget gate is used to decide which historical information to discard; the input gate is used to update the current important information; and the output gate is used to generate the final prediction result.

[0091] Compared to traditional linear models, LSTM models overcome the limitations of static threshold control, providing accurate data for the subsequent dynamic allocation of mains power, energy storage, and generators, thereby reducing users' total costs and carbon emissions and improving equipment utilization.

[0092] It should be noted that the LSTM model can be pre-trained. Specifically, a training dataset is obtained, which includes multiple training data points and their corresponding labels. In the context of the previous scenario, each training data point represents historical electricity consumption data and electricity demand information, while the corresponding label represents future electricity consumption data for different time periods within the same electricity consumption scenario. The training data from the training dataset is input into the initial model to obtain prediction data. Based on the prediction data and the corresponding labels, a loss function value is obtained, and the parameters in the initial model are trained and adjusted based on the loss function value. This training process is repeated until a well-trained prediction model is obtained.

[0093] Step 102. Determine the target power supply strategy for the electrical equipment based on the electricity consumption forecast curve, cost information, and preset constraints.

[0094] The target power supply strategy represents the supply of power to electrical equipment through different power supply methods at different times. These different power supply methods include: mains power supply, energy storage device power supply, and generator power supply.

[0095] For example, cost information refers to cost data involved in the power supply process. Specifically, cost information may include, but is not limited to, time-of-use electricity prices, generator fuel costs, and depreciation costs of power supply equipment.

[0096] Constraints refer to the restrictions that must be met during the generation of power supply strategies. Constraints ensure that all generated power supply strategies meet power demand and do not exceed equipment performance limitations.

[0097] Based on the electricity consumption forecast curve obtained in step 101, multiple power supply strategies that meet the constraints are generated based on the electricity consumption forecast curve and cost information, and the target power supply strategy is selected from these multiple power supply strategies. Optionally, multiple power supply strategies that meet the constraints can be generated through optimization algorithms, and the target power supply strategy can be finally generated through iterative calculation.

[0098] Step 103. According to the target power supply strategy, control at least one of the mains power, energy storage device and generator to supply power to the electrical equipment.

[0099] For example, a generator is a device used to convert mechanical energy into electrical energy, capable of generating electrical energy to supply power to electrical devices. In conjunction with the preceding steps, it can be understood that the target power supply strategy generated in step 102 refers to using different power supply methods to supply power to electrical devices at different times. Electrical devices refer to load devices that consume electrical energy in an electrical consumption scenario, i.e., the target devices connected to the power supply equipment.

[0100] Specifically, power is supplied to the electrical equipment according to the time period to which the current moment belongs and the power supply method indicated in the target power supply strategy. For example, when the time-of-use price of the mains electricity is undervalued, the mains power access switch is turned on to supply power to the electrical equipment through the mains electricity; when the remaining energy storage of the energy storage device is less than the minimum limit, the generator is started to supply power to the electrical equipment.

[0101] The power supply processing method provided in this application combines electricity demand forecasting with power supply strategy optimization to solve the problems of resource waste and increased costs caused by static power supply strategies. Specifically, the forecasting model generates an electricity demand forecast curve by analyzing historical electricity consumption data and electricity demand information, dynamically reflecting load changes at different times. Based on the electricity demand forecast curve, cost information is used for optimization to obtain the target power supply strategy for the electrical equipment; and based on the target power supply strategy, at least one of the following—mains power, energy storage devices, and generators—is controlled to supply power to the electrical equipment. By dynamically adapting to electricity demand, resource idleness of power supply equipment during low-load periods is avoided, while balancing power supply costs and equipment performance. Ultimately, this improves the rationality of the power supply strategy, ensures its flexibility and economy, significantly reduces operation and maintenance costs, and improves power supply efficiency.

[0102] Based on the aforementioned embodiments, an optimization function can be constructed according to the actual optimization objective. Using this optimization function, different combinations of power supply strategies are explored to find the power supply strategy that best represents the optimization objective, which is then used as the target power supply strategy.

[0103] In one example, step 102 in the above embodiment, in the process of determining the target power supply strategy for electrical equipment, may specifically include:

[0104] Based on a preset optimization function, the system performs iterative optimization based on electricity consumption forecast curves, cost information, and preset constraints to obtain the target power supply strategy for the electrical equipment.

[0105] The optimization function represents the total cost of supplying power to electrical equipment; the target power supply strategy is the power supply strategy with the minimum total cost represented by the optimization function among multiple power supply strategies; the constraints include at least one of the following: the power limit of the power supply equipment, the allowable range of remaining energy storage of the energy storage equipment, and the minimum operating time of the generator; the cost information includes at least one of the following: the time-of-use electricity price of the grid, the fuel cost of the generator, and the depreciation cost of the power supply equipment, which includes energy storage equipment and generator.

[0106] For example, an optimization function is a mathematical model used to quantify the cost of a power supply strategy. For instance, the function value corresponding to the optimization function is the sum of the mains electricity cost, generator fuel cost, and depreciation cost of the power supply equipment required under that power supply strategy. It can be understood that the mains electricity cost is calculated based on the time-of-use (TOU) price of mains electricity from the cost information, according to the power supply strategy. The TOU price refers to the price of mains electricity at different times. The generator fuel cost and the depreciation cost of the power supply equipment are directly obtained from the cost information. The fuel cost refers to the fuel cost consumed by the generator during operation, and the depreciation cost refers to the amortized cost over the service life of the power supply equipment (such as energy storage devices or generators).

[0107] Based on a preset optimization function, the electricity consumption forecast curve, cost information, and preset constraints are input, and the optimization algorithm iteratively optimizes the process to obtain the target power supply strategy for the electrical equipment. The optimization algorithm can be a Mixed-Integer Linear Programming (MILP) algorithm. The MILP algorithm can calculate the target power supply strategy according to the optimization function, thereby reducing fuel consumption.

[0108] The MILP algorithm is a mathematical optimization method used to find the optimal solution (such as the lowest cost or highest efficiency) under constraints that include both continuous and integer variables.

[0109] For example, the MILP algorithm, based on an optimization function, can simultaneously handle both integer and continuous variables in a power supply strategy. The integer variables in the power supply strategy include decision variables for whether to start the generator (e.g., a value of 1 indicates start, and a value of 0 indicates stop). The continuous variables in the power supply strategy include the power output of each power source.

[0110] Furthermore, under the constraints of the power limit of the power supply equipment, the allowable range of the remaining energy storage of the energy storage equipment, and the minimum operating time of the generator, multiple different power supply strategies are obtained with the goal of minimizing the total cost. The power supply strategy corresponding to the minimum function value of the optimization function (i.e. the minimum total cost value) is taken as the target power supply strategy.

[0111] Specifically, an exemplary target power supply strategy could be as follows: during peak electricity price periods, if the remaining energy stored in the energy storage device is sufficient, the device should be prioritized for power supply. If the electricity load exceeds the power supply capacity of the mains and the energy storage device, the generator should be automatically started and operated within the economic power range. This target power supply strategy addresses the hybrid optimization problem of discrete decision-making and continuous adjustment in multi-energy coordinated power supply, ensuring that the system achieves the lowest cost and efficient utilization of equipment under dynamic demand.

[0112] In this example, the allowable range of remaining energy storage refers to the range of state of charge (SOC) that the energy storage device is allowed to operate within. The minimum operating time of the generator refers to the shortest time that the generator must operate continuously to avoid frequent start-stop cycles that could affect the generator's lifespan.

[0113] Optionally, TinyML models (Tiny Machine Learning) can be deployed locally to analyze electricity load data in real time, triggering generator startup or energy storage device discharge even in the event of a network outage. TinyML models are deployed directly on edge devices, rather than relying on cloud servers for data processing and decision-making.

[0114] In the example above, the generation process of the target power supply strategy is refined by combining the optimization function with constraints. Specifically, the optimization function can quantify the total cost of different power supply strategies using cost information, and can select the power supply strategy with the minimum total cost. Furthermore, by combining constraints, it can be ensured that the target power supply strategy meets the power supply requirements and satisfies the performance limitations of the power supply equipment itself. On this basis, cost minimization is achieved.

[0115] Based on the above embodiments, since the power supply of energy storage devices may be unstable due to environmental factors during actual power supply, the allowable range of remaining energy storage can be adjusted according to relevant environmental data.

[0116] Figure 2 Flowchart of the power supply processing method provided in this application Figure 2 ,like Figure 2 As shown, the method also includes:

[0117] Step 201. Obtain temperature and humidity data of the environment where the energy storage device is located.

[0118] For example, temperature sensors and humidity sensors are configured at key locations in the environment where the energy storage device is located, to collect temperature and humidity data of the environment where the energy storage device is located, respectively.

[0119] Specifically, temperature data refers to the real-time temperature value of the environment in which the energy storage device is located, and humidity data refers to the real-time humidity value of the environment in which the energy storage device is located. For example, the ambient temperature and humidity of the outdoor environment in which lithium batteries are used during outdoor construction.

[0120] Step 202. Adjust the allowable range of remaining energy storage of the energy storage device based on temperature and humidity data.

[0121] For example, as can be seen from the foregoing examples, the allowable range of remaining energy storage of the energy storage device serves as a constraint on the power supply strategy. The ambient temperature and humidity data of the energy storage device are collected in real time by sensors, and the allowable range of remaining energy storage of the energy storage device is dynamically adjusted according to a preset environment-performance mapping relationship (such as lowering the SOC upper limit at low temperatures).

[0122] For example, in low-temperature environments for lithium batteries, the SOC (State of Charge) limit is lowered to prevent power outages caused by battery capacity degradation. Specifically, if the obtained ambient temperature is lower than a preset first temperature, the environment is considered low-temperature. In high-temperature environments, the discharge rate is limited to extend battery life. Specifically, if the obtained ambient temperature is higher than a preset second temperature, the environment is considered high-temperature. The second temperature is higher than the first temperature.

[0123] For example, if the obtained ambient humidity data is greater than the preset humidity threshold, the SOC upper limit of the energy storage device will be reduced.

[0124] In the above example, by monitoring the environmental parameters of the energy storage device and dynamically adjusting the constraints, the target power supply strategy can be corrected, thus solving the problem of power supply performance fluctuations in harsh environments. Real-time feedback of temperature and humidity data ensures the stability of the energy storage device's power supply performance in harsh environments.

[0125] exist Figure 1 As can be seen from the embodiments shown, in the process of supplying power to electrical equipment, power is supplied to the electrical equipment through at least one of mains power, energy storage equipment and generator.

[0126] Specifically, in one example, according to the target power supply strategy, the access switch connecting the mains power and the electrical equipment is turned on to enable the mains power to supply power to the electrical equipment.

[0127] For example, when the target power supply strategy indicates that the current time period should be powered by the mains power, the access switch connecting the mains power and the electrical equipment is turned on to enable the mains power to supply power to the electrical equipment.

[0128] For example, during periods of low electricity price (time-of-use pricing), the connection switch between the mains power supply and the electrical equipment can be opened to allow the mains power to supply electricity to the equipment, thus reducing mains electricity costs. Alternatively, the mains power can be controlled to charge energy storage devices. During periods of high electricity price, the connection switch between the mains power supply and the electrical equipment can be closed, and the energy storage devices can supply electricity to the equipment.

[0129] In another example, according to the target power supply strategy, the energy storage device is controlled to operate in a discharge mode so that the energy storage device can supply power to the electrical equipment.

[0130] For example, when the target power supply strategy indicates that the current time period should be powered by the energy storage device, the energy storage device is controlled to operate in discharge mode so as to enable the energy storage device to supply power to the electrical equipment.

[0131] For example, when the time-of-use electricity price of the mains is high, the energy storage device is controlled to operate in discharge mode, and at the same time, the connection switch between the mains and the electrical equipment is closed, so that the energy storage device supplies power to the electrical equipment.

[0132] In another example, the generator is controlled to start according to the target power supply strategy so that the generator can supply power to the electrical equipment.

[0133] For example, since the target power supply strategy is generated based on the electricity consumption forecast curve, it can identify which periods in the future will have higher electricity demand than the energy storage device can supply. The target power supply strategy records these periods of high electricity demand and controls the generator to start, thereby enabling the generator to supply power to the electrical devices.

[0134] It should be noted that the above three examples can be implemented individually or in combination. Specifically, in the process of combined implementation, different power supply methods can be switched to supply power to electrical equipment at different times according to the target power supply strategy.

[0135] For example, during periods of low electricity prices, grid power is prioritized for charging energy storage; during periods of high electricity prices, energy storage is prioritized for power supply; and in cases of overload, diesel generators are started to fill the gap. In addition, grid power serves as the primary power source, the energy storage system is used to smooth out short-term load fluctuations, and the diesel generator serves as a backup power source.

[0136] Optionally, the power grid access, energy storage system and diesel generator can be integrated through an energy management system, and the power supply mode can be dynamically adjusted based on the target power supply strategy to achieve seamless switching.

[0137] In the above embodiments, the target power supply strategy allows for flexible control of at least one of the following: mains power, energy storage devices, and generators, to supply power to the electrical equipment. Optionally, switching between different power supply methods is also possible, avoiding the limitation of using only a single and fixed power supply method in related technologies, thus solving the problem of poor rationality in power supply strategies. Furthermore, the target power supply strategy can minimize the total cost by selecting an appropriate power supply method to supply power to the electrical equipment, further improving the rationality of the power supply strategy.

[0138] As described in the preceding scenario, temporary power supply is typically achieved by leasing power equipment. However, leased equipment lacks full lifecycle tracking; usage records and health status are not effectively managed, making it difficult to assess its usability upon recycling. Therefore, blockchain technology can be introduced to record the leasing, recycling, and usage processes of power equipment through an electronic ledger, ensuring its immutability.

[0139] Figure 3 Flowchart of the power supply processing method provided in this application Figure 3 ,like Figure 3 As shown, the method also includes:

[0140] Step 301. Generate an electronic ledger for the power supply equipment based on the usage record data of the power supply equipment.

[0141] The power supply equipment includes energy storage devices and generators.

[0142] For example, the recorded data refers to the operating records of power supply equipment. Taking energy storage equipment as an example, the recorded data can characterize the number of times the energy storage equipment is charged and discharged; taking generators as an example, the recorded data can characterize the cumulative operating time of the generator.

[0143] Based on the usage records of power supply equipment, an electronic ledger can be generated. An electronic ledger refers to the usage records of power supply equipment stored via blockchain.

[0144] Optionally, the electronic ledger can also record the rental history and health status of the power supply equipment. The assessment indicators for health status include, but are not limited to, the degree of equipment aging and the number of failures.

[0145] Step 302. Store the electronic ledger in the blockchain and perform at least one preset action on the power supply equipment based on the electronic ledger in the blockchain.

[0146] The blockchain comprises multiple nodes, including: device user nodes, device provider nodes, and device testing nodes.

[0147] For example, blockchain is a distributed ledger technology used to store immutable electronic ledgers. A blockchain is composed of multiple nodes, each accessing the blockchain through authorization and verification, enabling them to collectively read and update the electronic ledger. It's important to note that each node in the blockchain leaves a record of each update and read operation, thus ensuring the accuracy and immutability of the electronic ledger.

[0148] In the practical application scenario of this application, temporary power supply can include both equipment users and equipment providers. Equipment users are entities or individuals needing to lease power supply equipment, while equipment providers are entities or individuals renting out power supply equipment. Both equipment users and providers access the blockchain through authorization and verification, acting as equipment user nodes and equipment provider nodes, respectively.

[0149] Optionally, a device testing node is also configured in the blockchain. The device testing node is used to perform performance testing and health assessments on the power supply equipment. It can be understood that the device testing node accesses the blockchain through authorization and verification.

[0150] Blockchain technology enables full lifecycle management and reliable traceability of power supply equipment in temporary power supply scenarios. Lessors (equipment providers), users (equipment users), and third-party testing agencies (equipment testers) are included as participating nodes in the blockchain network to jointly maintain an electronic ledger for the equipment. This records key information such as usage records, maintenance history, and operating status in real time, ensuring data transparency and immutability.

[0151] Based on this, preset actions are executed using the electronic ledger. Specifically, smart contracts are deployed in the blockchain, and through the management rules indicated by the smart contracts, preset actions are automatically executed on the power supply equipment according to the electronic ledger. The smart contracts can analyze the electronic ledger, and if the power supply equipment meets certain conditions, the preset actions are executed on the power supply equipment.

[0152] Specifically, at least one preset action includes at least one of the following actions:

[0153] When the service life of the power supply equipment is reached, a recycling command is sent to the power supply equipment to lock it.

[0154] Send the electronic ledger of the power supply equipment to the equipment testing node so that the equipment testing node can perform a health assessment of the power supply equipment and obtain the health assessment score of the power supply equipment.

[0155] When the health assessment score is less than a preset threshold, the status of the power supply equipment will be changed to unavailable.

[0156] For example, when leasing power supply equipment, there is usually a lease term. In the electronic ledger, the time when the user first used the power supply equipment is retrieved and compared with the current time. If the time interval between the two is greater than the time interval indicated by the lease term, a retrieval instruction is sent to the power supply equipment. This indicates that the lease for the current user has expired and the power supply equipment must be returned and can no longer be used. Specifically, a retrieval instruction is sent to the power supply equipment, and in response, the power supply equipment performs a locking operation. The locking operation includes, but is not limited to, locking the operation permissions of the power supply equipment and notifying the logistics provider to retrieve the power supply equipment.

[0157] For example, an electronic ledger of the power supply equipment is sent to the equipment monitoring node, enabling the monitoring node to perform a health assessment on the power supply equipment based on the electronic ledger, thereby obtaining a health assessment score. This health assessment score is used to quantify the health status of the power supply equipment.

[0158] Optionally, the equipment testing node can also generate a health assessment report for the power supply equipment based on the electronic ledger, including the performance loss and fault records of the power supply equipment.

[0159] For example, the health assessment score of the power supply equipment obtained by the equipment detection node can be compared with a preset threshold. If the health assessment score is less than the preset threshold, the status of the power supply equipment will be changed to unavailable to prevent inefficient equipment from entering the rental market.

[0160] Optionally, the equipment inspection node can also conduct tests on the power supply equipment itself after the logistics provider has recovered it. Specifically, by using indicators such as vibration sensors and insulation resistance testing, the remaining lifespan of the equipment can be quantified, supporting tiered recycling. For example, Level 1 equipment can be directly put into the next rental after simple maintenance; Level 2 equipment requires a certain degree of repair to meet rental requirements; and for power supply equipment that is unusable, valuable components and parts can be recycled for use in the manufacture of new equipment.

[0161] It should be noted that power supply equipment that is unavailable will not be leased by other equipment users. Therefore, in the process of generating the target power supply strategy, when minimizing the total cost based on the optimization function, all power supply equipment considered is in an available state.

[0162] It should be noted that the above-described exemplary preset actions can be implemented individually or in combination.

[0163] In the above embodiments, blockchain technology is introduced into the entire process of leasing, using, and recycling power supply equipment, realizing full lifecycle management of power supply equipment. By generating and storing electronic ledgers on the blockchain based on the usage record data of the power supply equipment, the immutability and transparency of the usage record data can be guaranteed.

[0164] Furthermore, by automatically executing preset actions on power supply equipment based on electronic ledgers, the management efficiency of the entire process of leasing, using, and recycling power supply equipment can be improved. This indirectly increases the reuse rate of power supply equipment while reducing management costs.

[0165] Building upon this, in one example, the method further includes:

[0166] Based on the electronic ledger of the power supply equipment, the behavior evaluation processes of the equipment user nodes and equipment provider nodes in the blockchain are carried out to obtain the behavior scores of the equipment user nodes and the behavior scores of the equipment provider nodes.

[0167] For example, behavioral assessment processing refers to credit scoring of equipment users and equipment providers based on electronic ledgers, such as the user's on-time return records. The resulting behavioral score is a quantified numerical value representing the creditworthiness of the equipment user or equipment provider.

[0168] It should be noted that the usage records of power supply equipment recorded in the electronic ledger also include behavioral data. For example, behavioral data includes records of timely return of equipment by the user and equipment maintenance records by the equipment provider.

[0169] For example, users who return equipment on time earn high credit scores, which in turn increases the behavioral score of their nodes. Similarly, equipment providers who maintain equipment in good condition improve their credit scores, thus increasing the behavioral score of their nodes.

[0170] Optionally, for the equipment provider node, the system checks whether the equipment status of the power supply equipment matches the equipment status recorded in the electronic ledger. If the recorded equipment statuses match, the behavior score of the equipment provider node is increased. The system also checks whether there is any false information in the equipment information recorded in the electronic ledger. If no false information is found, the behavior score of the equipment provider node is increased.

[0171] Optionally, for the equipment user node, the number of times the power supply equipment is used in accordance with regulations is monitored through an electronic ledger. If the number of times the equipment is used in accordance with regulations is greater than or equal to the preset limit, the behavior score of the equipment user node is increased. The return time of the power supply equipment recorded in the electronic ledger is compared with the lease expiration time. If there are no overdue returns, the behavior score of the equipment user node is increased.

[0172] For both equipment provider nodes and equipment user nodes, they can be categorized based on their behavioral scores. Nodes with a behavioral score greater than 90 are considered high-quality nodes, and these nodes will receive preferential rates, no margin deposit, and priority transaction matching. Nodes with a behavioral score less than 60 are considered high-risk nodes, and these nodes will be subject to measures such as increased margin deposits, higher rates, and restrictions on high-value transactions.

[0173] In the above example, by evaluating the behavior of both device user nodes and device provider nodes in the blockchain, the equipment leasing credit system can be improved. By quantifying behavioral scores, users are incentivized to use equipment responsibly, reducing the risk of equipment damage and enhancing the transparency and trustworthiness of power supply equipment distribution.

[0174] The power supply processing method provided in this application combines electricity demand forecasting with power supply strategy optimization to solve the problems of resource waste and increased costs caused by static power supply strategies. Specifically, the forecasting model generates an electricity demand forecast curve by analyzing historical electricity consumption data and electricity demand information, dynamically reflecting load changes at different times. Based on the electricity demand forecast curve, cost information is used for optimization to obtain the target power supply strategy for the electrical equipment; and based on the target power supply strategy, at least one of the following—mains power, energy storage devices, and generators—is controlled to supply power to the desired electrical equipment.

[0175] By dynamically adapting to electricity demand, the system avoids resource idleness of power supply equipment during low-load periods, while balancing power supply costs and equipment performance. Ultimately, this improves the rationality of power supply strategies, ensures their flexibility and economy, significantly reduces operation and maintenance costs, and enhances power supply efficiency.

[0176] By combining optimization functions with constraints, the total cost of different power supply strategies can be quantified. The power supply strategy with the minimum total cost can be selected as the power supply strategy, ensuring that the target power supply strategy meets the power supply requirements and satisfies the performance limitations of the power supply equipment itself.

[0177] By monitoring the environmental parameters of the energy storage device and dynamically adjusting the constraints, the target power supply strategy can be corrected, which can solve the problem of power supply performance fluctuation of the energy storage device in harsh environments and ensure the stability of the power supply performance of the energy storage device in harsh environments.

[0178] Based on the target power supply strategy, it is possible to flexibly control at least one of the following—mains power, energy storage devices, and generators—to supply power to electrical equipment. This avoids the problem of limited power supply methods in related technologies and addresses the issue of poor rationality in power supply strategies.

[0179] Introducing blockchain technology into the entire process of leasing, using, and recycling power supply equipment can ensure the immutability and transparency of usage records. It also improves the equipment leasing credit system. By quantifying behavioral scores, it incentivizes users to use equipment responsibly, reducing the risk of equipment damage.

[0180] Figure 4 A schematic diagram of the power supply processing device provided in this application is shown below. Figure 4 As shown, the power supply processing device 40 provided in this embodiment includes:

[0181] The acquisition module 401 is used to acquire historical electricity consumption data and electricity demand information, input the historical electricity consumption data and electricity demand information into the prediction model for processing, and obtain the electricity consumption prediction curve; wherein, the electricity consumption prediction curve represents the predicted electricity consumption data in different time periods;

[0182] The processing module 402 is used to determine the target power supply strategy for electrical equipment based on the electricity consumption forecast curve, cost information and preset constraints; wherein, the target power supply strategy represents the power supply to the electrical equipment through different power supply methods at different time periods, and the different power supply methods include: mains power supply, energy storage device power supply and generator power supply.

[0183] The control module 403 is used to control at least one of the mains power, energy storage device and generator to supply power to the electrical equipment according to the target power supply strategy.

[0184] In one possible implementation, based on the electricity consumption forecast curve, cost information, and preset constraints, a target power supply strategy for the electrical equipment is determined, and the processing module 402 is used to:

[0185] Based on a preset optimization function, and according to the electricity consumption forecast curve, cost information and preset constraints, iterative optimization is performed to obtain the target power supply strategy for the electrical equipment.

[0186] The optimization function represents the total cost of supplying power to electrical equipment; the target power supply strategy is the power supply strategy with the minimum total cost represented by the optimization function among multiple power supply strategies; the constraints include at least one of the following: the power limit of the power supply equipment, the allowable range of remaining energy storage of the energy storage equipment, and the minimum operating time of the generator; the cost information includes at least one of the following: the time-of-use electricity price of the grid, the fuel cost of the generator, and the depreciation cost of the power supply equipment, which includes energy storage equipment and generator.

[0187] In one possible implementation, the acquisition module 401 is further configured to acquire temperature and humidity data of the environment in which the energy storage device is located;

[0188] The processing module 402 is also used to adjust the allowable range of remaining energy storage of the energy storage device based on temperature data and humidity data.

[0189] In one possible implementation, according to a target power supply strategy, at least one of the mains power, energy storage devices, and generators is controlled to supply power to the electrical equipment. The control module 403 is used for:

[0190] According to the target power supply strategy, the connection switch for connecting the mains power to the electrical equipment is opened to enable the mains power to supply power to the electrical equipment; and / or,

[0191] According to the target power supply strategy, the energy storage device is controlled to operate in discharge mode to enable the energy storage device to supply power to the electrical equipment; and / or,

[0192] According to the target power supply strategy, the generator is controlled to start so that it can supply power to the electrical equipment.

[0193] In one possible implementation, the processing module 402 is further configured to:

[0194] Based on the usage records of the power supply equipment, an electronic ledger for the power supply equipment is generated; the power supply equipment includes energy storage equipment and generators.

[0195] The electronic ledger is stored in the blockchain, and at least one preset action is performed on the power supply equipment based on the electronic ledger in the blockchain; wherein the blockchain includes multiple nodes, including: equipment user node, equipment provider node, and equipment testing node.

[0196] In one possible implementation, at least one preset action includes at least one of the following actions:

[0197] When the service life of the power supply equipment is reached, a recycling command is sent to the power supply equipment to lock it.

[0198] Send the electronic ledger of the power supply equipment to the equipment testing node so that the equipment testing node can perform a health assessment of the power supply equipment and obtain the health assessment score of the power supply equipment.

[0199] When the health assessment score is less than a preset threshold, the status of the power supply equipment will be changed to unavailable.

[0200] In one possible implementation, the processing module 402 is further configured to:

[0201] Based on the electronic ledger of the power supply equipment, the behavior evaluation processes of the equipment user nodes and equipment provider nodes in the blockchain are carried out to obtain the behavior scores of the equipment user nodes and the behavior scores of the equipment provider nodes.

[0202] The power supply processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0203] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0204] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0205] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0206] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0207] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0208] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0209] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0210] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0211] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0212] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0213] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0214] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0215] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0216] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0217] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0218] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A power supply processing method, characterized in that, include: Historical electricity consumption data and electricity demand information are obtained, and the historical electricity consumption data and electricity demand information are input into the prediction model for processing to obtain an electricity consumption prediction curve; wherein, the electricity consumption prediction curve represents the predicted electricity consumption data in different time periods; Based on the electricity consumption forecast curve, cost information, and preset constraints, a target power supply strategy for the electrical equipment is determined; wherein, the target power supply strategy represents supplying power to the electrical equipment through different power supply methods at different time periods, and the different power supply methods include: mains power supply, energy storage device power supply, and generator power supply; According to the target power supply strategy, at least one of the mains power, the energy storage device, and the generator is controlled to supply power to the electrical equipment.

2. The method according to claim 1, characterized in that, Based on the electricity consumption forecast curve, cost information, and preset constraints, the target power supply strategy for the electrical equipment is determined, including: Based on a preset optimization function, and according to the electricity consumption forecast curve, the cost information, and preset constraints, an iterative optimization process is performed to obtain the target power supply strategy for the electrical equipment. Wherein, the optimization function represents the total cost of supplying power to the electrical equipment; the target power supply strategy is the power supply strategy with the minimum total cost represented by the optimization function among multiple power supply strategies; the constraints include at least one of the following: the power limit of the power supply equipment, the allowable range of the remaining energy storage of the energy storage equipment, and the minimum operating time of the generator; the cost information includes at least one of the following: the time-of-use electricity price of the mains electricity, the fuel cost of the generator, and the depreciation cost of the power supply equipment, wherein the power supply equipment includes energy storage equipment and generator.

3. The method according to claim 2, characterized in that, The method further includes: Acquire temperature and humidity data of the environment in which the energy storage device is located; The allowable range of remaining energy storage of the energy storage device is adjusted based on the temperature data and the humidity data.

4. The method according to claim 1, characterized in that, According to the target power supply strategy, controlling at least one of the mains power, the energy storage device, and the generator to supply power to the electrical device includes: According to the target power supply strategy, the access switch connecting the mains power to the electrical equipment is turned on, so as to enable the mains power to supply power to the electrical equipment; and / or, According to the target power supply strategy, the energy storage device is controlled to operate in discharge mode to enable the energy storage device to supply power to the electrical device; and / or, According to the target power supply strategy, the generator is controlled to start so that the generator can supply power to the electrical equipment.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: An electronic ledger for the power supply equipment is generated based on the usage record data of the power supply equipment; wherein, the power supply equipment includes energy storage equipment and generators; The electronic ledger is stored in a blockchain, and at least one preset action is performed on the power supply equipment based on the electronic ledger in the blockchain; wherein the blockchain includes multiple nodes, including: equipment user node, equipment provider node, and equipment testing node.

6. The method according to claim 5, characterized in that, The at least one preset action includes at least one of the following actions: When the service life of the power supply equipment is reached, a recycling command is sent to the power supply equipment to lock it. The electronic ledger of the power supply equipment is sent to the device detection node so that the device detection node can perform a health assessment of the power supply equipment and obtain a health assessment score for the power supply equipment. When the health assessment score is less than a preset threshold, the status of the power supply equipment will be changed to unavailable.

7. The method according to claim 5, characterized in that, The method further includes: Based on the electronic ledger of the power supply equipment, the behavior evaluation processes of the equipment user nodes and equipment provider nodes in the blockchain are performed respectively to obtain the behavior scores of the equipment user nodes and the behavior scores of the equipment provider nodes.

8. A power supply processing device, characterized in that, include: The acquisition module is used to acquire historical electricity consumption data and electricity demand information, input the historical electricity consumption data and the electricity demand information into the prediction model for processing, and obtain the electricity consumption prediction curve; wherein, the electricity consumption prediction curve represents the predicted electricity consumption data in different time periods; The processing module is used to determine the target power supply strategy for the electrical equipment based on the electricity consumption forecast curve, cost information, and preset constraints; wherein, the target power supply strategy represents supplying power to the electrical equipment through different power supply methods at different time periods, and the different power supply methods include: mains power supply, energy storage device power supply, and generator power supply; The control module is used to control at least one of the mains power, the energy storage device, and the generator to supply power to the electrical equipment according to the target power supply strategy.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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